






'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      '''gpt_neo'''
      UpperCamelCase_             =      ['''past_key_values''']
      UpperCamelCase_             =      {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		int=5_0257  ,   UpperCAmelCase      :		Optional[Any]=2048  ,   UpperCAmelCase      :		str=2048  ,   UpperCAmelCase      :		str=24  ,   UpperCAmelCase      :		Tuple=[[["global", "local"], 12]]  ,   UpperCAmelCase      :		int=16  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   UpperCAmelCase      :		Optional[int]=256  ,   UpperCAmelCase      :		List[str]="gelu_new"  ,   UpperCAmelCase      :		Optional[int]=0.0  ,   UpperCAmelCase      :		Dict=0.0  ,   UpperCAmelCase      :		int=0.0  ,   UpperCAmelCase      :		Optional[Any]=0.1  ,   UpperCAmelCase      :		Optional[Any]=1e-5  ,   UpperCAmelCase      :		Dict=0.0_2  ,   UpperCAmelCase      :		Dict=True  ,   UpperCAmelCase      :		int=5_0256  ,   UpperCAmelCase      :		List[str]=5_0256  ,   **UpperCAmelCase      :		List[str]  ,   )	->	Union[str, Any]:


       '''simple docstring'''

       lowercase       : Tuple						=vocab_size
       lowercase       : Optional[int]						=max_position_embeddings
       lowercase       : Tuple						=hidden_size
       lowercase       : str						=num_layers
       lowercase       : Optional[Any]						=num_heads
       lowercase       : List[Any]						=intermediate_size
       lowercase       : Union[str, Any]						=window_size
       lowercase       : Optional[Any]						=activation_function
       lowercase       : Union[str, Any]						=resid_dropout
       lowercase       : List[str]						=embed_dropout
       lowercase       : int						=attention_dropout
       lowercase       : List[str]						=classifier_dropout
       lowercase       : List[Any]						=layer_norm_epsilon
       lowercase       : int						=initializer_range
       lowercase       : List[Any]						=use_cache

       lowercase       : Union[str, Any]						=bos_token_id
       lowercase       : Optional[int]						=eos_token_id

       lowercase       : Tuple						=attention_types
       lowercase       : List[str]						=self.expand_attention_types_params(UpperCAmelCase		)

       if len(self.attention_layers		) != self.num_layers:
        raise ValueError(
            '''Configuration for convolutional module is incorrect. '''
            '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
            f'but is `len(config.attention_layers) = {len(self.attention_layers		)}`, '
            f'`config.num_layers = {self.num_layers}`. '
            '''`config.attention_layers` is prepared using `config.attention_types`. '''
            '''Please verify the value of `config.attention_types` argument.'''		)

       super().__init__(bos_token_id=UpperCAmelCase  ,   eos_token_id=UpperCAmelCase  ,   **UpperCAmelCase		)






      @staticmethod
      def 					A__   (			UpperCAmelCase      :		Dict		)	->	int:


       '''simple docstring'''

       lowercase       : int						=[]
       for item in attention_types:
        for _ in range(item[1]		):
         attentions.extend(item[0]		)
       return attentions





def 				lowercase_					(     __A			:							Any       ,   __A			:							Tuple       ,   __A			:							Tuple       ,   __A			:							Dict      )							->   List[str]:



 """simple docstring"""




 import torch

 lowercase       : str						=input.size()
 lowercase       : List[str]						=len(__A      )
 lowercase       : Optional[Any]						=shape[dimension]

 lowercase       : Optional[Any]						=torch.arange(0       ,   __A       ,   __A      )
 lowercase       : List[str]						=torch.div(sizedim - size       ,   __A       ,   rounding_mode='''floor'''      ) + 1
 lowercase       : Optional[int]						=torch.arange(__A      ) + low_indices[:min_length][:, None]

 lowercase       : List[Any]						=[slice(__A      )] * rank
 lowercase       : Dict						=indices
 lowercase       : str						=input[s]

 lowercase       : str						=list(range(0       ,   rank + 1      )      )
 perm.append(perm.pop(dimension + 1      )      )

 return sliced.permute(__A      )


def 				lowercase_					(     __A			:							Tuple       ,   __A			:							int      )							->   List[str]:



 """simple docstring"""




 import torch

 lowercase       : Any						=torch.arange(1       ,   __A      )
 lowercase       : Union[str, Any]						=torch.remainder(__A       ,   __A      )
 lowercase       : Any						=remainders == 0
 lowercase       : List[str]						=candidates[divisor_indices]
 lowercase       : Optional[int]						=torch.max(__A      )
 return largest_divisor, torch.div(__A       ,   __A       ,   rounding_mode='''floor'''      )





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      @property
      def 					A__   (			self      :		List[str]		)	->	Mapping[str, Mapping[int, str]]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
       if self.use_past:
        self.fill_with_past_key_values_(UpperCAmelCase  ,   direction='''inputs'''		)
        lowercase       : List[str]						={0: '''batch''', 1: '''past_sequence + sequence'''}
       else:
        lowercase       : Dict						={0: '''batch''', 1: '''sequence'''}

       return common_inputs






      @property
      def 					A__   (			self      :		int		)	->	int:


       '''simple docstring'''

       return self._config.num_heads






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		PreTrainedTokenizer  ,   UpperCAmelCase      :		int = -1  ,   UpperCAmelCase      :		int = -1  ,   UpperCAmelCase      :		bool = False  ,   UpperCAmelCase      :		Optional[TensorType] = None  ,   )	->	Mapping[str, Any]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=super(UpperCAmelCase  ,   self		).generate_dummy_inputs(
           UpperCAmelCase  ,   batch_size=UpperCAmelCase  ,   seq_length=UpperCAmelCase  ,   is_pair=UpperCAmelCase  ,   framework=UpperCAmelCase		)

       # We need to order the input in the way they appears in the forward()
       lowercase       : List[Any]						=OrderedDict({'''input_ids''': common_inputs['''input_ids''']}		)

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
        else:
         import torch

         lowercase	,		lowercase       : Tuple						=common_inputs['''input_ids'''].shape
         # Not using the same length for past_key_values
         lowercase       : Optional[Any]						=seqlen + 2
         lowercase       : List[str]						=(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         lowercase       : Optional[Any]						=[
             (torch.zeros(UpperCAmelCase		), torch.zeros(UpperCAmelCase		)) for _ in range(self.num_layers		)
         ]

       lowercase       : Any						=common_inputs['''attention_mask''']
       if self.use_past:
        lowercase       : List[Any]						=ordered_inputs['''attention_mask'''].dtype
        lowercase       : List[str]						=torch.cat(
            [ordered_inputs['''attention_mask'''], torch.ones(UpperCAmelCase  ,   UpperCAmelCase  ,   dtype=UpperCAmelCase		)]  ,   dim=1		)

       return ordered_inputs






      @property
      def 					A__   (			self      :		str		)	->	int:


       '''simple docstring'''

       return 13











"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[Any]          =		{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		['''PLBartTokenizer''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							List[str]          =		[
      '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''PLBartForCausalLM''',
      '''PLBartForConditionalGeneration''',
      '''PLBartForSequenceClassification''',
      '''PLBartModel''',
      '''PLBartPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_plbart import PLBartTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_plbart import (
        PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        PLBartForCausalLM,
        PLBartForConditionalGeneration,
        PLBartForSequenceClassification,
        PLBartModel,
        PLBartPreTrainedModel,
    )


else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)




